Csilla Farkas
Seniorforsker
(+47) 948 14 727
csilla.farkas@nibio.no
Sted
Ås - Bygg O43
Besøksadresse
Oluf Thesens vei 43, 1433 Ås (Varelevering: Elizabeth Stephansens vei 21)
Sammendrag
Small retention ponds are increasingly recognised as effective nature-based solutions for managing hydrological extremes in Norway’s agricultural catchments. Typically located in upper catchment areas or at the forest–agriculture interface, these ponds temporarily store runoff during intense rainfall events and snowmelt. In addition to flood mitigation, they provide important co-benefits by reducing soil erosion and sediment transport and by protecting agricultural drainage systems from erosion and overflow during extreme events, thereby supporting long-term soil productivity. Although individual storage volumes are limited, their cumulative impact at the catchment scale can be substantial when retention ponds are strategically distributed across the landscape. This study investigates the potential effects of small retention ponds using process-based hydrological modelling with SWAT+ to support catchment-scale climate adaptation planning in a Norwegian agricultural catchment. SWAT+ enables an improved representation of hydrological connectivity between managed landscapes and the stream network through its flexible spatial structure and rule-based management algorithms. The model is calibrated using a constraint-based approach that integrates both soft and hard data to represent streamflow and sediment dynamics in the Lierelva catchment. Multiple retention ponds are implemented to assess their cumulative effects on streamflow and sediment transport. Finally, the study discusses key challenges associated with modelling catchment–NBS interactions using SWAT+.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
ABSTRACT This study evaluated the SWAT+ model in a Norwegian catchment with mixed forest-agriculture land use, tile drainage, and multiple lakes, and examined the added value of incorporating soft data as process-based constraints during calibration. The primary aim was to test whether such constraints improve hydrological consistency in addition to statistical fit. A stepwise methodology was applied, including parameter initialization, model verification, water balance soft calibration, and constraint-based hard calibration. We showed how each stage incrementally improved model performance. Three hydrological constraints were defined to represent water balance components (runoff coefficient), streamflow signatures (baseflow index), and expert knowledge of catchment behavior (tile flow ratio). Constraint-based calibration achieved slightly lower efficiency scores (NSE = 0.61, KGE = 0.72) than unconstrained calibration (NSE = 0.65, KGE = 0.77), reflecting the trade-off between optimizing performance metrics and ensuring realistic hydrological processes. The baseflow index was the most influential constraint, eliminating about 77% of non-behavioral simulations when assessed individually. The results also highlight the importance of lake initialization and the need for multiple performance metrics when tuning lake release parameters. Overall, integrating process-based knowledge strengthened internal consistency and increased confidence that SWAT+ performs well for the right reasons.
Divisjon for miljø og naturressurser
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Divisjon for miljø og naturressurser
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Divisjon for miljø og naturressurser
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About the project
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ENGAGE: Europe Nutrient Management - Guided Approaches for Greater Export reduction
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Divisjon for matproduksjon og samfunn
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